Nonlinear techniques for source detection and localization in shallow ocean with non-Gaussian noise

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Author

Zahra Madadi Ardekani

Date of Issue

2013

School

School of Computer Engineering

Research Centre

Centre for Multimedia and Network Technology

Abstract

Acoustic waves in water are used to detect or locate targets, measure environmental or target parameters and transmit signals. Although detection and localization of an underwater source (target) have been the primary need in military applications, they have been found many commercial applications as well. In this work, we consider the problem of detection and localization of acoustic sources in the presence of non-Gaussian noise. Although assumption of Gaussian distribution for ambient noise provides simplicity and tractability, it is not a valid assumption when a large number of impulses appear in the background noise, such as warm water shallow oceans. Two main sources that contribute to the non-Gaussianity of ambient noise are biological noise produced by some marine organisms such as snapping shrimp, and navigation noise. When noise is non-Gaussian, the performance of linear signal processing techniques that have been derived based on the assumption of Gaussian noise characteristics degrade. In these cases, nonlinear processing techniques are more effective. We investigate a number of nonlinear methods for detecting and localizing acoustic sources in the presence of highly non-Gaussian noise. We present a robust and easily implementable detector based on nonlinear wavelet denoising (NWD) for detection of signals. A class of nonlinear median based transforms has been chosen for denoising since the conventional denoising techniques based on linear wavelet transforms are suitable only for denoising signals in Gaussian noise. The proposed NWD detector offers the following advantages for signal detection in strongly non - Gaussian noise: (1) significantly better performance than the matched filter, (2) greater robustness than the optimal detector, (3) moderate computational complexity. Further, we develop a computationally simple algorithm known as SAGE-USL, for 3-dimensional (3-D) localization of multiple acoustic sources in shallow ocean. In this algorithm, a hybrid array of sensors composed of a vertical and a horizontal linear array is used. In the SAGE-USL algorithm, after a rough estimate of different unknown parameters, a novel SAGE-based approach is applied to update the estimates sequentially. We further analyze the computational complexity of the proposed algorithm and compare it with that of 3-D MUSIC. Modified version of the SAGE-USL algorithm for an array of acoustic vector sensors is also presented. Simulations conducted to evaluate the performance of the algorithm illustrate: (1) Significant improvement in the source localization performance compared to 3-D MUSIC with RMSEs that compare favorably with the corresponding Cramer-Rao Bounds (CRBs) , (2) Fast convergence rate of the proposed algorithm, (3) Significant reduction in computational complexity compared to that of 3-D MUSIC. We also derive an expression for CRBs of 3-D localization of multiple sources in a range independent ocean with any symmetric noise distribution that is easy to compute and usable for different array configuration of scalar sensors and acoustic vector sensors. The CRBs derived are used for performance evaluation of SAGE-USL algorithm.